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单细胞转录组分析揭示 IRF1 驱动的上皮状态及糖胺聚糖-糖酵解偶联在顺铂耐药 HGSOC 中的作用

英文原题:Single-Cell Transcriptome Analysis Reveals IRF1-Driven Epithelial States and Glycosaminoglycan-Glycolysis Coupling in Cisplatin-Resistant HGSOC.

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Single-Cell Transcriptome Analysis Reveals IRF1-Driven Epithelial States and Glycosaminoglycan-Glycolysis Coupling in Cisplatin-Resistant HGSOC.

PubMed 2026/08/14(内容时间) Cancer Inform Q3 · IF 2.1(JCR 2025)

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研究概要

HGSOC 中的顺铂耐药性编码于离散的 IRF1 驱动的上皮状态中,这些状态由特定的 TME 通讯网络和糖胺聚糖-糖酵解代谢轴所支持。这一整合性单细胞信息学框架产生了可用于克服卵巢癌铂类耐药的可检验生物标志物和治疗靶点。

研究思路结论见上方概要

顺铂耐药是高级别浆液性卵巢癌(HGSOC)复发的主要原因,但 bulk 表达特征无法定位耐药恶性状态或维持这些状态的肿瘤微环境(TME)相互作用。本研究旨在通过整合多队列单细胞转录组与药物基因组学和临床数据,定义顺铂耐药上皮细胞状态及其调控和代谢回路。

我们从六个公开的单细胞 RNA 测序队列中,从 32 例 HGSOC 肿瘤中汇集了 159,419 个细胞,并进行了统一整合、聚类和谱系注释。通过使用 Scissor 将 scRNA-seq 数据与 癌症药物敏感性基因组学数据库 预测的顺铂反应评分值耦合,将顺铂反应映射到单细胞,并用基于 AUCell 的方法进行验证。随后,我们应用了基于受体-配体的细胞间通讯分析(CellChat)、转录因子(TF)活性推断(SCENIC 和基于 TRRUST 的 NetAct)、拟时序轨迹重建(Monocle3)以及通路水平的代谢评分。在卵巢癌细胞系和 The Cancer Genome Atlas(TCGA)HGSOC 队列中评估了与药物敏感性和患者结局的关联。

在34个上皮亚簇中,14个显著富集顺铂耐药表型,并共同构成了大多数预测耐药细胞。这些状态的转录特征为应激、干扰素和凋亡程序,并通过细胞外基质和黏附通路(例如LAMA3-CD44、COL6A1/2-CD44、NECTIN3-NECTIN2和CD99-CD99相互作用)与内皮细胞、成纤维细胞、髓系细胞和T/NK细胞形成密集的通讯枢纽。TF活性建模收敛于一个以IRF1为中心的调控程序,该程序沿上皮轨迹增加,并协调炎症和凋亡基因表达。在代谢方面,耐药富集的上皮状态显示糖胺聚糖生物合成选择性上调,尤其是硫酸角质素,并与糖酵解增强相耦合;糖酵解活性与细胞系中预测的顺铂反应评分以及IRF1/STAT1读出(GBP3)相关,后者在TCGA HGSOC中分层生存。

展开英文摘要原文

Cisplatin resistance is the principal cause of relapse in high-grade serous ovarian cancer (HGSOC), but bulk-expression signatures cannot localize resistant malignant states or the tumor-microenvironment (TME) interactions that sustain them. This study aimed to define cisplatin-resistant epithelial cell states and their regulatory and metabolic circuits by integrating multi-cohort single-cell transcriptomes with pharmacogenomic and clinical data.

We assembled 159,419 cells from 32 HGSOC tumors across six public single-cell RNA-sequencing cohorts and performed harmonized integration, clustering, and lineage annotation. Cisplatin response was mapped to single cells by coupling scRNA-seq data to Genomics of Drug Sensitivity in Cancer predicted cisplatin response score values using Scissor, with AUCell-based validation. We then applied receptor-ligand-based cell-cell communication analysis (CellChat), transcription-factor (TF) activity inference (SCENIC and NetAct with TRRUST), pseudotime trajectory reconstruction (Monocle3), and pathway-level metabolic scoring. Associations with drug sensitivity and patient outcome were evaluated in ovarian cancer cell lines and The Cancer Genome Atlas (TCGA) HGSOC cohort.

Among 34 epithelial subclusters, 14 were significantly enriched for a cisplatin-resistant phenotype and collectively accounted for most predicted resistant cells. These states were transcriptionally characterized by stress, interferon, and apoptotic programs and formed dense communication hubs with endothelial cells, fibroblasts, myeloid cells, and T/NK cells via extracellular-matrix and adhesion pathways (for example, LAMA3-CD44, COL6A1/2-CD44, NECTIN3-NECTIN2, and CD99-CD99 interactions). TF-activity modeling converged on an IRF1-centered regulatory program that increased along an epithelial trajectory and coordinated inflammatory and apoptotic gene expression. Metabolically, resistance-enriched epithelial states showed selective up-regulation of glycosaminoglycan biosynthesis, particularly keratan sulfate, coupled to heightened glycolysis; glycolytic activity correlated with predicted cisplatin response score in cell lines and an IRF1/STAT1 readout (GBP3) stratified survival in TCGA HGSOC.

Cisplatin resistance in HGSOC is encoded in discrete IRF1-driven epithelial states that are supported by specific TME communication networks and a glycosaminoglycan-glycolysis metabolic axis. This integrative single-cell informatics framework yields testable biomarkers and therapeutic targets for overcoming platinum resistance in ovarian cancer.

论文信息

作者
Jia Z、Pan W、Zhao X、Li L、Tan W
单位
Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.China
期刊
Cancer informatics2026
原文标识
PubMed 42605287 · DOI 10.1177/11769351261478408